View source for 18193982. REALISTIC, CONTROLLABLE AGENT SIMULATION USING GUIDED TRAJECTORIES AND DIFFUSION MODELS simplified abstract (NVIDIA Corporation)

=REALISTIC, CONTROLLABLE AGENT SIMULATION USING GUIDED TRAJECTORIES AND DIFFUSION MODELS=


==Organization Name==

[[:Category:NVIDIA Corporation|NVIDIA Corporation]]


[[Category:NVIDIA Corporation]]

==Inventor(s)==

[[:Category:Davis Winston Rempe of Redwood CA (US)|Davis Winston Rempe of Redwood CA (US)]][[Category:Davis Winston Rempe of Redwood CA (US)]]

[[:Category:Karsten Julian Kreis of Vancouver (CA)|Karsten Julian Kreis of Vancouver (CA)]][[Category:Karsten Julian Kreis of Vancouver (CA)]]

[[:Category:Sanja Fidler of Toronto (CA)|Sanja Fidler of Toronto (CA)]][[Category:Sanja Fidler of Toronto (CA)]]

[[:Category:Or Litany of Sunnyvale CA (US)|Or Litany of Sunnyvale CA (US)]][[Category:Or Litany of Sunnyvale CA (US)]]

[[:Category:Jonah Philion of Toronto (CA)|Jonah Philion of Toronto (CA)]][[Category:Jonah Philion of Toronto (CA)]]

==REALISTIC, CONTROLLABLE AGENT SIMULATION USING GUIDED TRAJECTORIES AND DIFFUSION MODELS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18193982 titled 'REALISTIC, CONTROLLABLE AGENT SIMULATION USING GUIDED TRAJECTORIES AND DIFFUSION MODELS

==Simplified Explanation==

The patent application relates to neural networks for agent simulation using guided trajectories.

* Neural networks are configured using training data including trajectories, state data of agents, and context data of the environment.
* Trajectories are determined using neural networks with guidance for controllability, such as waypoint navigation, obstacle avoidance, and group movement.

== Potential Applications ==
This technology could be applied in various fields such as autonomous vehicles, robotics, virtual reality simulations, and video game development.

== Problems Solved ==
1. Enhances the realism and controllability of agent simulations.
2. Improves navigation and interaction of agents in complex environments.

== Benefits ==
1. Increased accuracy and efficiency in agent simulations.
2. Enhanced user experience in virtual environments.
3. Improved safety and performance in autonomous systems.

== Potential Commercial Applications ==
"Enhancing Agent Simulation with Guided Trajectories: Applications in Autonomous Vehicles and Robotics"

== Possible Prior Art ==
There are existing systems and methods for agent simulation using neural networks, but the specific application of guided trajectories for controllability may be a novel approach.

=== Unanswered Questions ===

=== How does this technology handle dynamic environments and unpredictable agent behavior? ===
The patent application does not provide details on how the neural networks adapt to changes in the environment or agent behavior.

=== What are the computational requirements for implementing this technology in real-time applications? ===
The patent application does not address the computational resources needed to deploy this technology in real-time scenarios.


==Original Abstract Submitted==

In various examples, systems and methods are disclosed relating to neural networks for realistic and controllable agent simulation using guided trajectories. The neural networks can be configured using training data including trajectories and other state data associated with subjects or agents and remote or neighboring subjects or agents, as well as context data representative of an environment in which the subjects are present. The trajectories can be determining using the neural networks and using various forms of guidance for controllability, such as for waypoint navigation, obstacle avoidance, and group movement.



[[Category:G06N3/02]]